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Record W2117377188 · doi:10.5296/bmh.v3i2.8126

Benchmarking Characteristics of Steam Assisted Gravity Drainage (SAGD) Projects: Based On Interview Findings

2015· article· en· W2117377188 on OpenAlexafffundabout
Elias Ikpe, Jatinder Kumar, George Jergeas

Bibliographic record

VenueBusiness and Management Horizons · 2015
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBenchmarkingProcurementSteam-assisted gravity drainageEngineeringScheduleDuration (music)ProductivityBenchmark (surveying)Petroleum engineeringOperations managementWaste managementBusinessOil sandsComputer scienceEconomics

Abstract

fetched live from OpenAlex

SAGD is a relatively new method of oil extraction and recovery in Alberta oil and gas industry and the number of new SAGD plants in Alberta is expected to increase within this decade. The paper discusses the interviews finding of benchmarking characteristics of the SAGD projects. The research reviewed and analysed definition of capacity, main features, life cycle/ phases of SAGD projects and also major risks associated with it. A qualitative research methodology was employed in investigating the characteristics of SAGD projects. Interviews were conducted with industry practitioners, which contained open-ended questions. The result found the definition of capacity of SAGD projects is barrels/day and from the lifecycle of SAGD projects procurement/cosntruction phase is 75% of the total project cost while other phase in total constiutues the 25% of the total cost of the project. On the schedule prospective, procurement/construction phase constitute the 55% of the total project duration. This method has the potential to contribute to a reduction in cost and schedule overruns and improves SAGD project performance. It is concluded that the results of the study will help in achieving a higher rate of productivity in the Alberta oil and gas industry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.228
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes3
Has abstractyes

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